OpenRaiser/Intern-Atlas huggingface dataset:
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we introduce an ensemble method for convolutional neural network (CNN), called "virtual branching," which can be implemented with nearly no additional parameters and computation on top of standard CNNs. We propose our method in the context of person re-identification (re-ID). Our CNN model consists of shared bottom layers, followed by "virtual" branches, where neurons from a block of regular convolutional and fully-connected layers are partitioned into multiple sets. Each virtual branch is trained with different data to specialize in different aspects, e.g., a specific body region or pose orientation. In this way, robust ensemble representations are obtained against human body misalignment, deformations, or variations in viewing angles, at nearly no any additional cost. The proposed method achieves competitive performance on multiple person re-ID benchmark datasets, including Market-1501, CUHK03, and DukeMTMC-reID.
CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing
Venue: arXiv cs.DC 2018
Year: 2018

CIM/E is an easy and efficient electric power model exchange standard between different Energy Management System vendors. With the rapid growth of data size and system complexity, the traditional relational database is not the best option to store and process the data. In contrast, the graph database and graph computation show their potential advantages to handle the power system data and perform real-time data analytics and computation. The graph concept fits power grid data naturally because of the fundamental structure similarity. Vertex and edge in the graph database can act as both a parallel storage unit and a computation unit. In this paper, the CIM/E data is modeled into the graph database. Based on this model, the parallel network topology processing algorithm is established and conducted by applying graph computation. The modeling and parallel network topology processing have been demonstrated in the modified IEEE test cases and practical Sichuan power network. The processing efficiency is greatly improved using the proposed method.
A Review of Empirical Applications on Food Waste Prevention & Management
Venue: arXiv cs.CY 2018
Year: 2018

Food waste has a significant detrimental economic, environmental and social impact. Recent efforts in HCI re-search have examined ways of influencing surplus food waste management. In this paper, we conduct a research survey to investigate and compare the effectiveness of existing approaches in food waste management throughout its lifecycle from agricultural production, post-harvest handling and storage, processing, distribution and consumption. The objectives of the survey are 1) to identify methods in food waste management, 2) their area of focus, 3) the ICT techniques they apply, 4) and the food waste lifecycle they target. In addition, we analyse if 5) they provide an open access API for food waste data analysis. Based on the literature analysis, we then highlight their pros and cons with respect to applications in food waste management. The implications of this research could present a new opportunity for interested stack-holders and future technologies to play a key role in reducing domestic and national food waste.
Distributed Cache Enabled V2X Networks: Proposals, Research Trends and Challenging Issues
Venue: arXiv cs.NI 2018
Year: 2018

Nowadays, the internet of vehicles (IoV) has been evolved into the stage of vehicle to everything (V2X). However, the majority of existing work focuses on the motor-vehicles. In contrast, the sharing bicycle system is vastly and rapidly deployed as a feasible internet of things (IoT) application scene for the last mile problem (e.g., from station to home/office). Moreover, the internet access of current V2X is relied on the back-haul to roadside unit (RSU) connections. In this paper, other than prior work, we propose a versatile V2X system with a distributed framework and heterogeneous caching method. All the vehicles and devices on-the-road (motor-vehicle, non-motor-vehicle, pedestrian, etc.) are comprehensively included in the proposed networks. We further introduce a heterogeneous cache method for effective wireless transmission while utilizing the massive connected devices. The potential research trends on achieving high speed transmission, deep programming dedicated network slicing are highlighted as well as the big data, machine learning (ML), fog computing based image recognition and reconstruction, to provide some insights for future studies. Finally, the challenging issues, i.e., urban street canyon path loss and channel models, ultra reliable communication and low latency requirements, are discussed.
Reconfiguring spanning and induced subgraphs
Venue: arXiv cs.DS 2018
Year: 2018

Subgraph reconfiguration is a family of problems focusing on the reachability of the solution space in which feasible solutions are subgraphs, represented either as sets of vertices or sets of edges, satisfying a prescribed graph structure property. Although there has been previous work that can be categorized as subgraph reconfiguration, most of the related results appear under the name of the property under consideration; for example, independent set, clique, and matching. In this paper, we systematically clarify the complexity status of subgraph reconfiguration with respect to graph structure properties.
Renewable Energy Integration in Distribution System -- Synchrophasor Sensor based Big Data Analysis, Visualization, and System Operation
Venue: arXiv cs.SY 2018
Year: 2018

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster computing framework based on Apache Spark is considering to discover the hidden knowledge of the bag-data. And the data is transmitted with a Open System Interconnection (OSI) model to the data visualization platform in a high-speed communication architecture. Google Earth and global geographic information system (GIS) are used to design the visualization platform and realize the result with the test bench. The alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. The proposed network reconfiguration is solved in parallel manner with the limited switches and the strong computing capability. Further more, a multi-timescale operation approach is modeled with a three-phase distributed system, which consists the hourly scheduling at substation level and the minutes power flow operation at feeder level. The system cost with renewable generation is minimized at the substation level. The given error distribution model of renewable generation is simulated with a chance constraint, and the derived deterministic form is modeled with Gaussian mixture model (GMM) with genetic algorithm-based expectationmaximization (GAEM). The system cost is further reduced with the OPF in real-time (RT) scheduling. The semidefinite programming (SDP) is used to relax the nonconvexity of the three-phase unbalanced distribution system into a convex problem, which make sure to achieve the global optimal result. In the parallel manner, the ADMM is realizing getting the results in a short time.
Sampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm
Venue: arXiv cs.CG 2018
Year: 2018

We study the problem of decomposing a volume bounded by a smooth surface into a collection of Voronoi cells. Unlike the dual problem of conforming Delaunay meshing, a principled solution to this problem for generic smooth surfaces remained elusive. VoroCrust leverages ideas from $α$-shapes and the power crust algorithm to produce unweighted Voronoi cells conforming to the surface, yielding the first provably-correct algorithm for this problem. Given an $ε$-sample on the bounding surface, with a weak $σ$-sparsity condition, we work with the balls of radius $δ$ times the local feature size centered at each sample. The corners of this union of balls are the Voronoi sites, on both sides of the surface. The facets common to cells on opposite sides reconstruct the surface. For appropriate values of $ε$, $σ$ and $δ$, we prove that the surface reconstruction is isotopic to the bounding surface. With the surface protected, the enclosed volume can be further decomposed into an isotopic volume mesh of fat Voronoi cells by generating a bounded number of sites in its interior. Compared to state-of-the-art methods based on clipping, VoroCrust cells are full Voronoi cells, with convexity and fatness guarantees. Compared to the power crust algorithm, VoroCrust cells are not filtered, are unweighted, and offer greater flexibility in meshing the enclosed volume by either structured grids or random samples.
A constant-ratio approximation algorithm for a class of hub-and-spoke network design problems and metric labeling problems: star metric case
Venue: arXiv cs.DM 2018
Year: 2018

Transportation networks frequently employ hub-and-spoke network architectures to route flows between many origin and destination pairs. Hub facilities work as switching points for flows in large networks. In this study, we deal with a problem, called the single allocation hub-and-spoke network design problem. In the problem, the goal is to allocate each non-hub node to exactly one of given hub nodes so as to minimize the total transportation cost. The problem is essentially equivalent to another combinatorial optimization problem, called the metric labeling problem. The metric labeling problem was first introduced by Kleinberg and Tardos in 2002, motivated by application to segmentation problems in computer vision and related areas. In this study, we deal with the case where the set of hubs forms a star, which arises especially in telecommunication networks. We propose a polynomial-time randomized approximation algorithm for the problem, whose approximation ratio is less than 5.281. Our algorithms solve a linear relaxation problem and apply dependent rounding procedures.
On Combination Networks with Cache-aided Relays and Users
Venue: arXiv cs.IT 2018
Year: 2018

Caching is an efficient way to reduce peak hour network traffic congestion by storing some contents at the user's cache without knowledge of later demands. Coded caching strategy was originally proposed by Maddah-Ali and Niesen to give an additional coded caching gain compared the conventional uncoded scheme. Under practical consideration, the caching model was recently considered in relay network, in particular the combination network, where the central server communicates with $K=\binom{H}{r}$ users (each is with a cache of $M$ files) through $H$ immediate relays, and each user is connected to a different $r-$subsets of relays. Several inner bounds and outer bounds were proposed for combination networks with end-user-caches. This paper extends the recent work by the authors on centralized combination networks with end-user caches to a more general setting, where both relays and users have caches. In contrast to the existing schemes in which the packets transmitted from the server are independent of the cached contents of relays, we propose a novel caching scheme by creating an additional coded caching gain to the transmitted load from the server with the help of the cached contents in relays. We also show that the proposed scheme outperforms the state-of-the-art approaches.
Digital Limits of Government: The Failure of E-Democracy
Venue: arXiv cs.CY 2018
Year: 2018

While the Internet is often touted as a revolutionary technology, it might be noted that democratic institutions have witnessed no digital revolution through the Internet. This observation leads this chapter to argue that the field of e-democracy has generally failed to live up to its own reformist rhetoric. It argues that instead of reforming government processes through technology, e-democracy projects have tended to focus either on lowering the costs and increasing the efficiency of existing political processes or on analysing the civic participation that occurs outside of purpose-built e-democracy platforms. The chapter suggests that this lack of attention to the Internet's potential for systemic change in formal political institutions has little normative impact on the democratization of society and may even re-enforce, rather than challenge, the sociopolitical status quo. Further, it suggests that the current approach of e-democracy risks normalizing the Internet to the norms and expectations of the offline world. To elucidate this argument, this chapter overviews both the general trend of e-democracy projects and criticisms of those projects. Finally, the chapter proposes a more radical vision of e-democracy that, it suggests, would usher a larger potential for democratization. This more radical vision of e-democracy consists of recognizing the attributes of the Internet that transcend the limits of the analogue world and applying these to democracy. Such an approach would open the path for envisaging new political processes and systems, allowing the field of e-democracy to live up to its own rhetoric, and affording society the means to address multiple of the centuries-old problems faced by democracy.
Smartphone based Driving Style Classification Using Features Made by Discrete Wavelet Transform
Venue: arXiv cs.HC 2018
Year: 2018

Smartphones consist of different sensors, which provide a platform for data acquisition in many scientific researches such as driving style identification systems. In the present paper, smartphone data are used to evaluate the driving styles based on maneuvers analysis. The data obtained for each maneuver is the speed of the vehicle steering and the vehicle's direct and lateral acceleration. To classify the drivers based on their driving style, machine-learning algorithms can be used on these data. However, these data usually contains more information than it is needed and cause a bad effect on the learning accuracy. In addition, they may transfer some wrong information to the learning algorithm. Thus, we used Haar discrete wavelet transformation to remove noise effects. Then, we get the discrete wavelet transformation with four levels from smartphone sensors data, which include low-to-high frequencies, respectively. The obtained features vector for each maneuver includes the raw signal variance as well as the variance of the wavelet transform components. On these vectors, we use the k-nearest neighbors algorithm for features selection. Then, we use SVM, RBF and MLP neural networks on these features to separate braking and dangerous speed maneuvers from the safe ones as well as dangerous turning, U-turn and lane-changing maneuvers. The results are very interesting.
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
Venue: arXiv cs.CV 2018
Year: 2018

When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs
Venue: arXiv cs.RO 2018
Year: 2018

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...]
Consistent sets of lines with no colorful incidence
Venue: arXiv cs.CG 2018
Year: 2018

We consider incidences among colored sets of lines in $\mathbb{R}^d$ and examine whether the existence of certain concurrences between lines of $k$ colors force the existence of at least one concurrence between lines of $k+1$ colors. This question is relevant for problems in 3D reconstruction in computer vision.
A mullti- or many- objective evolutionary algorithm with global loop update
Venue: arXiv cs.NE 2018
Year: 2018

Multi- or many-objective evolutionary algorithm- s(MOEAs), especially the decomposition-based MOEAs have been widely concerned in recent years. The decomposition-based MOEAs emphasize convergence and diversity in a simple model and have made a great success in dealing with theoretical and practical multi- or many-objective optimization problems. In this paper, we focus on update strategies of the decomposition- based MOEAs, and their criteria for comparing solutions. Three disadvantages of the decomposition-based MOEAs with local update strategies and several existing criteria for comparing solutions are analyzed and discussed. And a global loop update strategy and two hybrid criteria are suggested. Subsequently, an evolutionary algorithm with the global loop update is implement- ed and compared to several of the best multi- or many-objective optimization algorithms on two famous unconstraint test suites with up to 15 objectives. Experimental results demonstrate that unlike evolutionary algorithms with local update strategies, the population of our algorithm does not degenerate at any generation of its evolution, which guarantees the diversity of the resulting population. In addition, our algorithm wins in most instances of the two test suites, indicating that it is very compet- itive in terms of convergence and diversity. Running results of our algorithm with different criteria for comparing solutions are also compared. Their differences are very significant, indicating that the performance of our algorithm is affected by the criterion it adopts.
Distributed Scheduling Algorithms for Optimizing Information Freshness in Wireless Networks
Venue: arXiv cs.IT 2018
Year: 2018

Age of Information (AoI), measures the time elapsed since the last received information packet was generated at the source. We consider the problem of AoI minimization for single-hop flows in a wireless network, under pairwise interference constraints and time varying channel. We consider simple, yet broad, class of distributed scheduling policies, in which a transmission is attempted over each link with a certain attempt probability. We obtain an interesting relation between the optimal attempt probability and the optimal AoI of the link, and its neighboring links. We then show that the optimal attempt probabilities can be computed by solving a convex optimization problem, which can be done distributively.
Optimizing Age of Information in Wireless Networks with Perfect Channel State Information
Venue: arXiv cs.IT 2018
Year: 2018

Age of information (AoI), defined as the time elapsed since the last received update was generated, is a newly proposed metric to measure the timeliness of information updates in a network. We consider AoI minimization problem for a network with general interference constraints, and time varying channels. We propose two policies, namely, virtual-queue based policy and age-based policy when the channel state is available to the network scheduler at each time step. We prove that the virtual-queue based policy is nearly optimal, up to a constant additive factor, and the age-based policy is at-most factor 4 away from optimality. Comparing with our previous work, which derived age optimal policies when channel state information is not available to the scheduler, we demonstrate a 4 fold improvement in age due to the availability of channel state information.
Attack Trees in Isabelle
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we present a proof theory for attack trees. Attack trees are a well established and useful model for the construction of attacks on systems since they allow a stepwise exploration of high level attacks in application scenarios. Using the expressiveness of Higher Order Logic in Isabelle, we succeed in developing a generic theory of attack trees with a state-based semantics based on Kripke structures and CTL. The resulting framework allows mechanically supported logic analysis of the meta-theory of the proof calculus of attack trees and at the same time the developed proof theory enables application to case studies. A central correctness and completeness result proved in Isabelle establishes a connection between the notion of attack tree validity and CTL. The application is illustrated on the example of a healthcare IoT system and GDPR compliance verification.
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) method, we develop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which is especially important in array signal processing. The MVALSE shares the advantages of the VALSE method, such as automatically estimating the model order, noise variance, weight variance, and providing the uncertain degrees of the frequency estimates. It is shown that the MVALSE can be viewed as applying the VALSE with single measurement vector (SMV) to each snapshot, and combining the intermediate data appropriately. Furthermore, the Seq-MVALSE is developed to perform sequential estimation. Finally, numerical results are conducted to demonstrate the effectiveness of the MVALSE method, compared to the state-of-the-art methods in the MMVs setting.
LoRa Throughput Analysis with Imperfect Spreading Factor Orthogonality
Venue: arXiv cs.NI 2018
Year: 2018

LoRa is one of the promising techniques for enabling Low Power Wide Area Networks (LPWANs) for future Internet-of-Things (IoT) devices. Although LoRa allows flexible adaptations of coverage and data rates, it is subject to intrinsic types of interferences: co-SF interferences where end-devices with the same Spreading Factors (SFs) are subject to collisions, and inter-SF interferences where end-devices with different SFs experience collisions. Most current works have considered perfect orthogonality among different SFs. In this work, we provide a theoretical analysis of the achievable LoRa throughput in uplink, where the capture conditions specific to LoRa are included. Results show the accuracy of our analysis despite approximations, and the throughput losses from imperfect SF orthogonality, under different SF allocations. Our analysis will enable the design of specific SF allocation mechanisms, in view of further throughput enhancements.